The most popular adversarial attack, Fast Gradient Sign Method (FGSM) has been proposed by Ian J. Goodfellow [1]}. The method works as follows: Suppose \(x \& y\)  are the input images and corresponding labels respectively, the weights of the network is represented by \(\theta \)  and the loss function is \(L(\theta , x, y)\)  . First, the gradient of the loss function for input image is calculated. The \(\nabla \)  operator takes out the derivative of the loss function. It gives a shape matrix including width, height, channels, and the tangent slopes. But most importantly only the sign of the slopes is needed to know when the pixel values increase or decrease. Then, these slope signs are multiplied by a very tiny imperceptible perturbation value \(\epsilon \)  . The resultant pattern is then added to the original inputs to create respective adversarial examples. The mathematical representation of this procedure is as follows:
\(\centering x_{adv} = x + \epsilon * sign(\nabla _{x}L(\theta ,x,y))\) 
